An Explorable Multiverse Analysis of Durante et al. (2013)

knit_as_emar()

Introduction

Steegen and colleagues [1] introduced the concept of multiverse analysis, which they illustrated by re-analyzing data from a 2013 paper by Durante and colleagues [2] entitled “The fluctuating female vote: Politics, religion, and the ovulatory cycle”. In this paper, we reproduce a small part of Steegen et al.’s multiverse analysis of Durante et al.’s study using explorable explanations. The data processing options can be selected interactively, which allows us to show the interaction plot reported in Durante et al. in addition to the p-value.

Analysis

The default analysis below reflects the choices made by Durante et al. [2]. Other options reflect alternatives considered by Steegen et al. [1]. Much of the text below is copied from Steegen et al. [1], in order to give an idea of what their article could have looked liked had they used explorable explanations. We first begin by loading the data used in the analysis and transforming six variables (Abortion, StemCell…, Profit) and creating three new variables (FiscConsComp, SocConsComp, RelComp).

data("durante")

data.raw.study2 <- durante %>%
  mutate(
    Abortion = abs(7 - Abortion) + 1,
    StemCell = abs(7 - StemCell) + 1,
    Marijuana = abs(7 - Marijuana) + 1,
    RichTax = abs(7 - RichTax) + 1,
    StLiving = abs(7 - StLiving) + 1,
    Profit = abs(7 - Profit) + 1,
    FiscConsComp = FreeMarket + PrivSocialSec + RichTax + StLiving + Profit,
    SocConsComp = Marriage + RestrictAbortion + Abortion + StemCell + Marijuana,
    RelComp = round((Rel1 + Rel2 + Rel3)/3, 2)
  )

To implement a multiverse analysis, we first define the multiverse object:

M = multiverse()

Fertility

Durante et al. classify women into a high or low fertility group based on cycle day. There are different reasonable ways of estimating a woman’s next menstrual onset, which is an intermediate step in determining cycle day. The perform this calculation in two ways

df = data.raw.study2 %>%
    mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))

df = data.raw.study2 %>%
    mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))

df = data.raw.study2 %>%
    mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
    mutate(NextMenstrualOnset = StartDateNext) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))

df = data.raw.study2 %>%
    mutate( ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast )  %>%
    mutate(NextMenstrualOnset = branch(menstrual_calculation,
        "mc_option1" ~ StartDateofLastPeriod + ComputedCycleLength,
        "mc_option2" ~ StartDateofLastPeriod + ReportedCycleLength,
        "mc_option3" ~ StartDateNext)
    )  %>%
    mutate(
      CycleDay = 28 - (NextMenstrualOnset - DateTesting),
      CycleDay = ifelse(WorkerID == 15, 11, ifelse(WorkerID == 16, 18, CycleDay)),
      CycleDay = ifelse(CycleDay > 1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28))
    )

The classification of women into a high or low fertility group based on cycle day can be done in several ways :

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))

df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))

df = df %>%
    mutate( Fertility = branch( fertility,
        "fer_option1" ~ factor( ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 25, "low", NA)) ),
        "fer_option2" ~ factor( ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 27, "low", NA)) ),
        "fer_option3" ~ factor( ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >= 18 & CycleDay <= 25, "low", NA)) ),
        "fer_option4" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low") ),
        "fer_option5" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low") )
    ))

Relationship status

There are at least three options for the dichotomization of women’s relationship status into single or committed :

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))

df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))

df = df %>%
    mutate(RelationshipStatus = branch(relationship_status,
        "rs_option1" ~ factor(ifelse(Relationship==1 | Relationship==2, 'Single', 'Relationship')),
        "rs_option2" ~ factor(ifelse(Relationship==1, 'Single', 'Relationship')),
        "rs_option3" ~ factor(ifelse(Relationship==1, 'Single', ifelse(Relationship==3 | Relationship==4, 'Relationship', NA))) )
    )

Exclusion criteria

The assignment of the participants to a high or low fertility group automatically excludes women whose cycle days are not in the high or low fertility range .

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter(TRUE)

df = df %>%
    filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)

df = df %>%
    filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

df = df %>%
    filter( branch(cycle_length, 
      "cl_option1" ~ TRUE,
      "cl_option2" ~ ComputedCycleLength > 25 & ComputedCycleLength < 35,
      "cl_option3" ~ ReportedCycleLength > 25 & ReportedCycleLength < 35
    ))

Because a lot of this data involves participants’ self reported measures, it is not unreasonable to exclude participants who are unsure of their responses :

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
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    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
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df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter(TRUE)

df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)

df = df %>%
    dplyr::filter( branch(certainty,
        "cer_option1" ~ TRUE,
        "cer_option2" ~ Sure1 > 6 | Sure2 > 6
    ))

Results

The authors evaluate the impact of a participants Fertility, RelationshipStatus and it’s interaction on their Religiosity which is defined using the composite variable RelComp.

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
    group_by(Fertility, RelationshipStatus) %>%
    mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
        "Single")) %>%
    summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
        .groups = "drop") %>%
    ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
    ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
    p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
    y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()

fit_RelComp <- lm( RelComp ~ 0 + Fertility * RelationshipStatus, data = df )

p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, " *")

broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "In Relationship", "Single")) %>%
  summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower), .groups = "drop") %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.5) +
  geom_linerange(aes(ymin = .lower, ymax = .upper), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0("Interaction: ", p_val), x = 1.5, y = 7.5, colour = "#666666") +
  labs(x = "Relationship Status", y = "Religiosity Composite Score") +
  ylim(c(0, 8.1)) +
  theme_minimal()

The interaction between relationship status and fertility in study 1 is shown in Figure 1. This plot reproduces Figure 1 from Durante et al.’s article [2] but with the y-axis starting at zero. This figure does not appear Steegen et al.’s multiverse analysis [1], as there would be 180 such figures to show, which would be impractical with a static paper. The p-value for the interaction is also shown on Figure 1.

Discussion

As in the original paper, we can see that “the multiverse analysis revealed that almost all choice combinations for data processing lead to large p values” [1] and we can again conclude that “the effect of fertility on religion seems too sensitive to arbitrary choices and thus too fragile to be taken seriously” [1]. Figure 1 can animated by holding the ‘A’ key, giving a striking demonstration of the variability of effect sizes across the multiverse that can usefully complement Steegen et al’s histogram of p-values.